ArticlePloS one2018
Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm.Bioengineering (Basel, Switzerland) · 2023Article
- COVID-19's Impact on International Trade.Entropy (Basel, Switzerland) · 2022Article
- Fibrosis Protein-Protein Interactions from Google Matrix Analysis of MetaCore Network.International journal of molecular sciences · 2021Article
- Assembling Disease Networks From Causal Interaction Resources.Frontiers in genetics · 2021Review
- Interdependence of Sectors of Economic Activities for World Countries from the Reduced Google Matrix Analysis of WTO Data.Entropy (Basel, Switzerland) · 2020Article
- Collective intelligence defines biological functions in Wikipedia as communities in the hidden protein connection network.PLoS computational biology · 2020Article
- MetaboRank: network-based recommendation system to interpret and enrich metabolomics results.Bioinformatics (Oxford, England) · 2019Article
- Interactions of pharmaceutical companies with world countries, cancers and rare diseases from Wikipedia network analysis.PloS one · 2019Article
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Authors and funding
3 authors.
Funding
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Abstract
Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical "reduced Google matrix" method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way.
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Registered trials
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